ADSNPheno: A computational pipeline of multi-omics analysis to predict gene regulatory networks from disease variants to phenotypes with applications in Alzheimer’s disease and SARS-CoV-2

Summary

Genome-wide association studies (GWAS) have found many genetic risk variants associated with Alzheimer’s disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various disease phenotypes is still limited. To address these problems, we developed a computational pipeline of integrated multi-omics analysis from genotype, transcriptomics, epigenomics to phenotypes for revealing gene regulatory mechanisms from disease variants to phenotypes.

Method

This pipeline, ADSNPheno, aims to predict gene regulatory networks of AD risk Single-Nucleotide Polymorphisms (SNPs) to different AD phenotypes. In particular, ADSNPheno first clusters gene co-expression networks and identifies the gene modules for various AD phenotypes. ADSNPheno further predicts the transcription factors (TFs) that significantly regulate the genes in each module, as well as the AD SNPs interrupting the TF binding sites on the regulatory elements. Finally, ADSNPheno constructs a full gene regulatory network linking SNPs, interrupted TFs, and regulatory elements to target genes for each phenotype. This network thus provides mechanistic insights of gene regulation from disease risk variants to AD phenotypes.

Please note our pipeline, ADSNPheno:

Hardware Requirements

Please note that this analysis is based on R 4.0. You will only need a standard computer with enough RAM to support the operations. For predicting gene regulatory networks, a Linux system with 32 GB RAM and 32GB storage would be enough to support.

Software Requirements

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